4th Down Bot is a Super Bowl Must-Have

It’s Sunday. The sun has set. Katy Perry and all that is horrible about corporate America’s involvement with the Super Bowl have gone with it. You’re stuck on the couch, beyond bloated from stuffing your trap with pigs in a blanket, chips and guacamole and seven Bud Light Platinums. The Seattle Seahawks hold a 17-14 lead with seven minutes remaining in the third quarter. Tom Brady and his New England Patriots failed to convert on third down and goal at Seattle’s four yard line. Take the sure three points against Seattle’s defense, right? No, no. Go for it on fourth down, because if the Pats don’t make it, Seattle has horrible field position.

What do you do? Flip a coin? It’s hard to think through the fog of booze and carbs. “What do you do?” asks Dennis Hopper’s Howard Payne from 1994’s blockbuster Speed. “What do you do?”

Refer to 4th Down Bot. He’s got all the fourth down answers.

Produced by The New York Times in 2013 and upgraded for this NFL season, 4th Down Bot utilizes a model created by Brian Burke of Advanced Football Analytics and ten years of data to live-critique coaches’ decisions via the Bot’s web site and on TwitterThe Times published its methodology here. It bases decisions on expected points, which measures the average number of points each situation is worth. The creators admit the model is similar to that developed by David Romer, a University of California, Berkeley economics professor who authored a paper in 2002 exploring  fourth down options. The Times notes more seasons of data differentiate the two models.

4thdownbotgraph
Image via The New York Times

“The game is ball possession, and coaches are losing sight of that,” David Leonhardt, editor at The Times, told Bill Littlefield of Only A Game.

The model assumes both the offense and defense are league average, with its goal of scoring as many points as possible. But once the fourth quarter hits, winning becomes the priority. The Bot measures how often teams won following a punt, field goal kick or fourth down attempt using data from NFL games played previously played.

Here is an example from The Times:

A field goal is worth 3 points, if it’s successful. But there is a catch: after scoring, you must kick off to your opponent, which, on average, will begin its drive on the 22-yard line. Judging from the chart above, a first-and-10 from your 22 is worth about 0.4 points. To NYT 4th Down Bot, a field goal is worth 3 points minus the cost of kicking off: 3 – 0.4 = 2.6 points. (Similarly, a touchdown and extra point is worth 7 – 0.4 = 6.6 points.)

What if the kicker misses? It’s a long field goal, about 55 yards, and the success rate of 55-yard field goals is only about 40 percent.

If the kick is no good, the opponent takes over on its 45-yard line. From our chart above, a first-and-10 from there is worth about 1.8 points. In this case, however, it is a first down for your opponent, so the point value from your perspective is –1.8 points.

NYT 4th Down Bot uses the expected points from success, the expected points from failure and the likelihood of each outcome to compute the net value of a decision.

Per the scenario above, 4th Down Bot would have kicked the field goal and settled for a 17-17 tie with 22 minutes of championship football left.

To be certain, the Bot is a fun tool for couch coaches everywhere rather than a serious, analytical decision-maker. It has its holes (it still doesn’t know what teams are playing, their strengths, or injuries involved). But when you want to look smart in front of friends and family this Sunday following a huge fourth down play, just whip out the Bot. He’s got your back.

“What do you do?” Payne asks again.

Take the three points.

Image via M P R





Seth loves baseball and anything with Sriracha in it. Follow him on Twitter @sethkeichline.

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AC
9 years ago

Related!
http://fivethirtyeight.com/features/kickers-are-forever/
538 whipped up a model to do (mostly) the same thing (only comparing FG to punt) and compared that to the NYT bot. The takeaway is that the NYT is about 5-10 years behind on modeling the quality of kickers, so they are underestimating the expected points on that side of the equation.
The fifth graphic in the link tells the story best.